A polynomial approximation algorithm for the problem is given.
同时，还给出了该问题的多项式时间近似算法。
The general methods are polynomial approximation and Chebyshev approximation.
常见的方法有多项式逼近，切比雪夫多项式逼近。
The polynomial approximation method is also employed to obtain simple formulas for frequency and amplitude correction.
本文利用多项式逼近方法获得了频率和幅值修正的计算公式。
Understand the degree of polynomial approximation and the effective range with the polynomial fitting order of any changes.
了解多项式的逼近程度和有效拟合区间随多项式的阶数有何变化。-1。
This result can be combined with subdivision method to obtain a piecewise interval polynomial approximation for a rational surface.
这一结果可以与细分技术相结合，得到有理曲面的分片区间多项式的逼近。
Because only two points a straight decisions, and in between the two curves can be used an infinite number of cubic polynomial approximation.
因为，两点只能决定一条直线，而在两点间的曲线可用无限多的三次多项式近似。因此，为使结果具有唯一性。
Orthogonal polynomial approximation is proposed to estimate the nonlinear deterministic trend and then the residual is used to test the unit root.
本文研究用正交多项式逼近非线性趋势，然后对残差进行单位根检验的方法。
This design dispenses with ROM which mainly limits the converting speed of DDS, but USES SIN function polynomial approximation directly generating digital SIN wave.
利用正弦多项式近似直接产生数字正弦波，而无需限制相幅转化速度的ROM。
The trackability limitation of current gradient algorithm is discussed. A new algorithm, named variable parameter gradient estimation algorithm with local polynomial approximation is proposed.
本文分析了梯度辨识算法跟踪时变系统的缺点，提出了一种新的基于局部多项式逼近的变参数梯度估计算法。
With the best polynomial approximation as a metric, the rate of approximation of the neural networks with single hidden layer to a continuous function is estimated by using a constructive approach.
以最佳多项式逼近为度量，用构造性方法估计单隐层神经网络逼近连续函数的速度。
Subsequently we present local approximation spaces of some interpolation polynomial.
随后又给出了几类插值多项式的局部近似空间。
By use of numerical approximation method, the paper USES the polynomial to approach segmentally the transient electric field from dipole dipole array.
本文采用数值逼近的方法，逐段用多项式表示偶极—偶极排列的瞬变电场。
Finally, based on the theorem, a polynomial 2 approximation algorithm for the location problem is presented.
最后，基于此定理，给出了选址问题的一个多项式2近似算法。
The paper emphasizes on the methods of polynomial surface approximation which is part of numerical approximations, and analyzes the precision of polynomial function model through the measured data.
着重介绍了数值逼近方法中的多项式曲面函数模型逼近法，并通过实测数据对多项式函数模型的精度进行分析。
A polynomial time approximation scheme (PTAS) for this problem is presented.
给出了一个多项式时间近似方案(PTAS)。
For both problems, we study their computational complexity and present optimal algorithms or polynomial time approximation algorithms.
并且对这两类问题都研究了他们的计算复杂性并给出了最优算法或者多项式时间近似算法。
Using the modulus of smoothness and K-functional, direct and inverse theorems of simultaneous approximation for a kind of multivariate trigonometric polynomial operators are established.
利用光滑模和K -泛函给出了一类多元三角多项式算子同时逼近的正逆定理。
The algorithm is polynomial curve fitting based on least square approximation. The experimental result shows its performance is more effect than that of the piecewise linear algorithm.
该算法是在最小二乘意义上的多项式曲线拟合，实验结果表明，其校正效果明显优于分段线性校正算法。
Theorem2.1: Algorithm2.1 is a Polynomial time approximation scheme Theorem2.2 When Algorithm2.1 end, the path between each node pairs which.
定理2.1：算法2.1是一个多项式时间近似方案定理2.2程序结束时每个要求所对应的两点之间的路径是唯一的。
In this paper, the function approximation of Gelenbe Neural Network (GNN) is discussed and it is proved that GNN can approximate any G-type polynomial by using constructional method.
该文研究了G神经网络的函数映射能力，给出了前馈g神经网络映射任意G型多项式的构造性证明。
The predictive intelligent control scheme is proposed, which is based on approximation theory and numerical method of Chcbyshev orthogonal polynomial.
本文基于切比雪夫正交多项式数值逼近方法，提出预测智能控制算法。
For no-waited model, we show it is strongly NP-hard, and present a pseudo-polynomial time optimal algorithm and an approximation algorithm with worst-case ratio 5/3.
对于不可等待的情况证明了它是强NP-难的，并给出了动态规划算法和一个最坏情况界为5/3的近似算法。
For no-waited model, we show it is strongly NP-hard, and present a pseudo-polynomial time optimal algorithm and an approximation algorithm with worst-case ratio 5/3.
对于不可等待的情况证明了它是强NP-难的，并给出了动态规划算法和一个最坏情况界为5/3的近似算法。
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